Circulating lymphocytes and monocytes transcriptomic analysis of patients with type 2 diabetes mellitus, dyslipidemia and periodontitis

Abstract

Type 2 diabetes mellitus (T2DM), dyslipidemia and periodontitis are frequently associated pathologies; however, there are no studies showing the peripheral blood transcript profile of these combined diseases. Here we identified the differentially expressed genes (DEGs) of circulating lymphocytes and monocytes to reveal potential biomarkers that may be used as molecular targets for future diagnosis of each combination of these pathologies (compared to healthy patients) and give insights into the underlying molecular mechanisms of these diseases. Study participants (n = 150) were divided into groups: (H) systemically and periodontal healthy (control group); (P) with periodontitis, but systemically healthy; (DL-P) with dyslipidemia and periodontitis; (T2DMwell-DL-P) well-controlled type 2 diabetes mellitus with dyslipidemia and periodontitis; and (T2DMpoorly-DL-P) poorly-controlled type 2 diabetes mellitus with dyslipidemia and periodontitis. We preprocessed the microarray data using the Robust Multichip Average (RMA) strategy, followed by the RankProd method to identify candidates for DEGs. Furthermore, we performed functional enrichment analysis using Ingenuity Pathway Analysis and Gene Set Enrichment Analysis. DEGs were submitted to pairwise comparisons, and selected DEGs were validated by quantitative polymerase chain reaction. Validated DEGs verified from T2DMpoorly-DL-P versus H were: TGFB1I1, VNN1, HLADRB4 and CXCL8; T2DMwell-DL-P versus H: FN1, BPTF and PDE3B; DL-P versus H: DAB2, CD47 and HLADRB4; P versus H: IGHDL-P, ITGB2 and HLADRB4. In conclusion, we identified that circulating lymphocytes and monocytes of individuals simultaneously affected by T2DM, dyslipidemia and periodontitis, showed an altered molecular profile mainly associated to inflammatory response, immune cell trafficking, and infectious disease pathways. Altogether, these results shed light on novel potential targets for future diagnosis, monitoring or development of targeted therapies for patients sharing these conditions.

CpG Single-Site Methylation Regulates TLR2 Expression in Proinflammatory PBMCs From Apical Periodontitis Individuals

“…PBMCs, which include monocytes, natural killers, and T and B lymphocytes, are key cells in the preservation of systemic homeostasis and defense against infection. Furthermore, their cytokine production has been proposed to reflect the activity of several immune-inflammatory diseases (20)(21)(22)(23). Our results showed that PBMCs from AP individuals have a global proinflammatory secretory profile with significantly higher levels of TNF-a, IL-1b, and IL-6 in comparison with healthy controls.…”

“…In this context, the internalization of bacteria or their antigens might result in mononuclear cell activation via TLR2 recognition (19), and reasonably contribute to a higher systemic inflammatory burden. Despite the evidence that supports the participation of PBMCs in systemic immune-inflammatory diseases, such as rheumatoid arthritis or cardiovascular diseases (20)(21)(22)(23), the behavior and regulatory mechanisms of PBMCs in AP are currently unknown. In this study, we aimed to determine the TLR2 gene methylation and inflammatory profiles of PBMCs in AP.…”

Safety and Transcriptome Analysis of Live Attenuated Brucella Vaccine Strain S2 on Non-pregnant Cynomolgus Monkeys Without Abortive Effect on Pregnant Cynomolgus Monkeys

“…We did not examine the lymph nodes for the innate immune response due to our concern that the fixation of animals and the collection of blood may lead to unexpected abortion, and as a consequence, it may interfere with our evaluations on the safety of the S2 vaccine, since abortion is an important parameter in our evaluation in this study. The use of the peripheral blood for the isolation of lymphocytes for transcriptomic analysis is not new, as the use of the peripheral blood from pigs ( 30 ), dogs ( 31 ), Chinese geese ( 32 ), and humans ( 33 – 35 ), including patients with COVID-19 ( 36 ), have been well-documented.…”

Using association rule mining to jointly detect clinical features and differentially expressed genes related to chronic inflammatory diseases

“…The Robust Multichip Average (RMA) strategy was used to preprocess raw .CEL files [41,42]. This strategy performs background correction through a normal-exponential convolution model, quantile normalizes the probe intensities and summarizes them into probesetlevel quantities using an additive model fit through the median-polish strategy [43]. The gene expression dataset is available in S2 File.…”